
Kriging: Using a GP to estimate a response surface and corresponding uncertainty from a number of shape exemplars and corresponding simulations results is known as Kriging [1]}. This response surface can then be used to optimize shapes using Bayesian Optimization. As discussed in Section REF , it is a widely used practice and is a standard approach to BO.

GCNN: An alternative to using GPs to estimate the response surface is to use a standard GCNN [2]}, [3]}, as discussed in Section REF . Since a GCNN does not return uncertainties, the optimization is performed using deterministic gradient-descent.

Ours-MCD: Our method when using MC-Dropout to estimate uncertainties.

Ours-Ens: Our method when using deep ensembles to estimate uncertainties.

